Jinghui Chen

Assistant Professor,
College of Information Sciences and Technology,
Penn State University

Email: jzc5917 [at] psu [dot] edu

I am an Assistant Professor in the College of Information Sciences and Technology at Penn State University. I received my Ph.D. in the Department of Computer Science, University of California, Los Angeles working with Dr. Quanquan Gu in 2021. I received my B.E. in the Department of Electrical Engineering and Information Science at the University of Science and Technology of China in 2015.

My research interests broadly include the theory and applications in different aspects of machine learning (machine learning robustness, machine learning efficiency, machine learning safety, etc.), especially adversarial machine learning.


Openings: I’m looking for highly motivated students (including PhDs, Masters, undergraduates), postdocs, and interns to join my group. If you’re interested in joining my lab, please fill and see instructions in the following form (feel free to skip optional questions).


News and Announcement

[09/2021] Our paper is accepted to NeurIPS 2021:

  • "Do Wider Neural Networks Really Help Adversarial Robustness?"
  • [06/2021] I recieved UCLA Outstanding Graduate Student Research Award.

    [04/2021] I will join the College of Information Sciences and Technology (IST) at Penn State University (PSU) in Fall 2021 as a tenure-track assistant professor.

    [07/2020] Released Model Robustness (ADBD) Leaderboard under RayS attack:

  • Benchmarking state-of-the-art robust trained models with ADBD metric
  • [05/2020] Our paper is accepted to KDD 2020:

  • "RayS: A Ray Searching Method for Hard-label Adversarial Attack"
  • [04/2020] We just launched a project using machine learning and AI to combat Covid-19!


    Publications (*equal contribution)

  • Boxi Wu*, Jinghui Chen*, Deng Cai, Xiaofei He and Quanquan Gu, Do Wider Neural Networks Really Help Adversarial Robustness? , In Proc. of the 35th Advances in Neural Information Processing Systems (NeurIPS), Virtual, 2021. [Paper]
  • Jinghui Chen, Yuan Cao and Quanquan Gu, Benign Overfitting in Adversarially Robust Linear Classification, ICML 2021 Workshop on Overparameterization: Pitfalls and Opportunities.
  • Difan Zou, Lingxiao Wang, Pan Xu, Jinghui Chen, Weitong Zhang and Quanquan Gu, Epidemic Model Guided Machine Learning for COVID-19 Forecasts in the United States , ICLR 2021 Workshop on Machine Learning for Preventingand Combating Pandemics. [Paper]
  • Estee Y Cramer, Jinghui Chen, et al., Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US , medRxiv: 2021.02.03.21250974, 2021. [Paper]
  • Katriona Shea, Jinghui Chen, et al., COVID-19 reopening strategies at the county level in the face of uncertainty: Multiple Models for Outbreak Decision Support , medRxiv: medRxiv:2020.11.03.20225409, 2020. [Paper]
  • Dongruo Zhou*, Jinghui Chen*, Yuan Cao*, Yiqi Tang, Ziyan Yang, and Quanquan Gu, On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization , NeurIPS 2020 Workshop on Optimization for Machine Learning. [Paper]
  • Jinghui Chen, Yu Cheng, Zhe Gan, Quanquan Gu and Jingjing Liu, Efficient Robust Training via Backward Smoothing , arXiv:2010.01278, 2020. [Paper]
  • COVID-19 Forecast Hub Consortium, Jinghui Chen, Ensemble Forecasts of Coronavirus Disease 2019 (COVID-19) in the U.S. , medRxiv:2020.08.19.20177493, 2020. [Paper]
  • Jinghui Chen and Quanquan Gu, RayS: A Ray Searching Method for Hard-label Adversarial Attack , in Proc of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), San Diego, CA, USA 2020. [Paper] [Code]

    A short version of this paper also appears on ICML 2020 Workshop on Uncertainty & Robustness in Deep Learning and ECCV 2020 Workshop on Adversarial Robustness in the Real World.

  • Jinghui Chen, Dongruo Zhou, Yiqi Tang, Ziyan Yang, Yuan Cao and Quanquan Gu, Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks , In Proc. of 29th International Joint Conference on Artificial Intelligence (IJCAI), Yokohama, Japan, 2020. [Paper] [Code]
  • Xiao Zhang*, Jinghui Chen*, Quanquan Gu and David Evans, Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models , In Proc of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), Palermo, Sicily, Italy, 2020. [Paper] [Code]
  • Jinghui Chen, Dongruo Zhou, Jinfeng Yi and Quanquan Gu, A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks , In Proc. of the 34th Conference on Artificial Intelligence (AAAI), New York, New York, USA, 2020. [Paper] [Code]
  • Pan Xu*, Jinghui Chen*and Quanquan Gu, Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization, In Proc. of the 32nd Advances in Neural Information Processing Systems (NIPS), Montréal, Canada, 2018. (Spotlight, 3.5%) [Paper]
  • Jinghui Chen, Pan Xu, Lingxiao Wang, Jian Ma and Quanquan Gu, Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization , in Proc. of the 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, 2018. (Long Oral Presentation, 4.8%) [Paper] [Code]
  • Jinghui Chen and Quanquan Gu, Fast Newton Hard Thresholding Pursuit for Sparsity Constrained Nonconvex Optimization, in Proc of the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Halifax, Nova Scotia, Canada, 2017. [Paper]
  • Jinghui Chen, Lingxiao Wang, Xiao Zhang, and Quanquan Gu, Robust Wirtinger Flow for Phase Retrieval with Arbitrary Corruption , arXiv:1704.06256, 2017. [Paper]
  • Jinghui Chen, Saket Sathe, Charu Aggarwal, and Deepak Turaga, Outlier Detection with Autoencoder Ensembles, in Proc of the 17th SIAM International Conference on Data Mining (SDM), Houston, Texas, USA, 2017. [Paper]
  • Jinghui Chen and Quanquan Gu, Stochastic Block Coordinate Gradient Descent for Sparsity Constrained Optimization, in Proc of the 32th International Conference on Uncertainty in Artificial Intelligence (UAI), New York / New Jersey, USA, 2016. [Paper]
  • Florian Baumann, Jinghui Chen, Karsten Vogt, and Bodo Rosenhahn, Improved threshold Selection by using Calibrated Probabilities for Random Forest Classifiers in Proc of the 12th Conference on Computer and Robot Vision (CRV), Halifax, Nova Scotia, Canada, 2015. [Paper]

  • Teaching

    Instructor

    Fall 2021

    Course: IST 597 Special Topics: Adversarial Machine Learning

    Penn State University

    Teaching Assistant

    Spring 2021

    Course: Fundamentals of Artificial Intelligence

    UCLA

    Teaching Assistant

    Winter 2020

    Course: Fundamentals of Artificial Intelligence

    UCLA

    Teaching Assistant

    Fall 2020

    Course: Introduction to Algorithms and Complexity

    UCLA

    Teaching Assistant

    Spring 2019

    Course: Special Topics in Computer Science: Machine Learning

    UVa

    Teaching Assistant

    Fall 2018

    Course: Special Topics in Computer Science: Machine Learning

    UVa

    Guest Lecturer

    Spring 2018

    Course: Machine Learning

    UVa

    Teaching Assistant

    Fall 2016

    Course: Optimization for Machine Learning

    UVa

    Teaching Assistant

    Spring 2016

    Course: Data Engineering

    UVa

    Teaching Assistant

    Fall 2015

    Course: Practice and Application of Data Science

    UVa


    Works

    08/2021 - Now

    Assistant Professor, Penn State University

    06/2020 - 09/2020

    Research Intern, Microsoft Research, Redmond, WA, USA

    06/2019 - 08/2019

    Machine Learning Intern, Twitter, San Francisco, CA, USA

    06/2018 - 08/2018

    Research Intern, JD.COM Silicon Valley Research Center, Mountain View, CA, USA

    06/2016 - 08/2016

    Research Intern, IBM T.J Watson Research Center, Yorktown Height, New York, USA

    02/2015 - 06/2015

    Research Intern, University of Hannover, Germany

    07/2014 - 08/2014

    Research Intern, University of Birmingham, England


    Honors and Awards

  • UCLA Outstanding Graduate Student Research Award, 06/2021
  • NeurIPS 2020 Student Travel Award, 10/2020
  • UCLA Graduate Division Fellowship, 09/2020
  • KDD 2020 Student Travel Award, 08/2020
  • NIPS 2019 Student Travel Award, 12/2019
  • NIPS 2018 Student Travel Award, 12/2018
  • ICML 2018 Student Travel Award, 07/2018
  • KDD 2017 Student Travel Award, 08/2017
  • SDM 2017 Student Travel Award, 04/2017
  • "Xing Ye" Scholarship, 09/2014
  • Third prize of “Huawei” Intelligent System Design Contest, 06/2014
  • National Second prize of Contemporary Undergraduate Mathematical Contest in Modeling, 10/2013
  • Top-notch Student Project Scholarship in Rudimentary Subject by Ministry of Education, 09/2013
  • "Zhang Zongzhi" Scientific Scholarship, 09/2013
  • Honorable Mention of Mathematical Contest in Modeling, 04/2013
  • Outstanding Student Scholarship (gold award), 10/2012
  • Outstanding Freshmen Scholarship (bronze award), 09/2011

  • Professional Activities

    Senior Program Committee

  • International Joint Conference on Artificial Intelligence (IJCAI)
  • AAAI Conference on Artificial Intelligence (AAAI)
  • Program Committee/Reviewer

  • IEEE International Conference on Big Data (BigData)
  • International Conference on Machine Learning (ICML)
  • Neural Information Processing Systems (NeurIPS)
  • International Conference on Artificial Intelligence and Statistics (AISTATS)
  • International Conference on Learning Representations (ICLR)
  • Journal Reviewer

  • IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
  • IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
  • IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)
  • IEEE Transactions on Knowledge and Data Engineering (TKDE)
  • Journal of Artificial Intelligence Research (JAIR)
  • Journal of Industrial and Management Optimization (JIMO)
  • Journal of Computational and Applied Mathematics
  • PLOS ONE
  • Neural Networks (NEUNET)
  • Neurocomputing
  • Machine Learning